Behavioural change phases of different stakeholders involved in the implementation process of ergonomics measures in bricklaying

The objective of this qualitative study was to assess whether a hypothesised sequential order of behavioural change phases would be fulfilled in different groups of stakeholders involved at the start of a process to implement ergonomic [corrected] measures in bricklaying teams. The measures include trestles, bricklaying scaffolds, mast climbing work platforms [corrected] and cranes. The behavioural change phases were: (1) being aware of measures, (2) understanding measures, (3) wanting measures, (4) intention to buy or hire measures, (5) ability to use measures, (6) using measures (experience), and (7) continuing to use measures. Structured interviews were conducted to examine the change phases in two groups of stakeholders (employers/work planners (n=11) [corrected] and foremen/bricklayers (n=9) [corrected] from nine companies) thought to be relevant in the decision to adopt and use the ergonomic [corrected] measures. The results show that the fulfilled behavioural change phases differ between individual stakeholders, groups of stakeholders, companies and also between ergonomic measures. The hypothesised order of fulfilled consecutive behavioural change phases for individual stakeholders has not been confirmed by this study. The relationship between [corrected] fulfilled and unfulfilled change phases by each stakeholder (group) and actual use of each ergonomic measure requires further study, so as to improve the selection of suitable implementation strategies [corrected]     

Box-Jenkins identification revisited—Part III : Multivariable systems

Part I of this series of three papers handles the identification of single input single output Box-Jenkins models on arbitrary frequency grids in an open and closed loop setting. Part II discusses the computational aspects and illustrates the theory on simulations and a real life problem. This paper extends the results of Parts I and II to multiple input multiple output systems. Contrary to the classical time domain approach, the presented technique does not require symbolic calculus for multiple output polynomial Box-Jenkins models.     

Airline planning benchmark problems—Part I:: Characterising networks and demand using limited data

This paper is the first of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on the analysis of airline data. Then a multi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II extends this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.